[FieldTrip] Comparison of measures of electrophysiological connectivity

pascualm at key.uzh.ch pascualm at key.uzh.ch
Fri Nov 2 02:36:01 CET 2018

Dear Colleagues,
The preprint entitled:
"A comparison of bivariate frequency domain measures of
electrophysiological connectivity"
might be of interest to those performing research related to
electrophysiological connectivity inference.
The abstract can be found below.
Cordially, Roberto
Roberto D. Pascual-Marqui, PhD, PD
The KEY Institute for Brain-Mind Research, University of Zurich
Visiting Professor at Neuropsychiatry, Kansai Medical University, Osaka

The problem of interest here concerns electrophysiological signals
from two cortical sites, acquired as invasive intracranial recordings,
or from non-invasive estimates of cortical electric neuronal activity
computed from EEG or MEG recordings (see e.g.
https://doi.org/10.1101/269753). In the absence of other sources,
these measured signals consist of an instantaneous linear mixture of
the true, actual, unobserved local signals, due to low spatial
resolution and volume conduction. A connectivity measure is unreliable
as a true indicator of electrophysiological connectivity if it is not
invariant to mixing, or if it reports a significant connection for a
mixture of independent signals. In (Vinck et al 2011 Neuroimage
55:1548) it was shown that coherence, imaginary coherence, and phase
locking value are not invariant to mixing, while the phase lag index
(PLI) and the weighted version (wPLI) are invariant to mixing. Here we
show that the lagged coherence (LagCoh) measure (2007,
https://arxiv.org/abs/0711.1455), not studied in Vinck et al, is
invariant to mixing. Additionally, we present here a new
mixture-invariant connectivity statistic: the "standardized imaginary
covariance" (sImCov). We also include in the comparisons the directed
PLI (dPLI) by Stam et al (2012 Neuroimage 62:1415). Fourier
coefficients for "N" trials are generated from a linear unidirectional
causal time domain model with electrophysiological delay "k" and
regression coefficient "b". 1000 random data sets of "N" trials are
simulated, and for each one, and for each connectivity measure,
non-parametric randomization tests are performed. The "true positive
detection rate" is calculated as the fraction of 1000 cases that have
significant connectivity at p<0.05, 0.1, and 0.2. The connectivity
methods were compared in terms of detection rates, under non-mixed and
mixed conditions, for small and large sample sizes "N", with and
without jitter, and for different values of signal to noise. Under
mixing, the results show that LagCoh outperforms wPLI, PLI, dPLI, and
sImCov. Without mixing, LagCoh and sImCov outperform wPLI, PLI, and
dPLI. Finally, it is shown that dPLI is an invalid estimator of flow
direction, i.e. it reverses and "goes against the flow" by merely
changing the sign of one of the time series, a fact that violates the
basic definition of Granger causality.

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